Introduction

Digital engineering and data intelligence have been gradually yet steadily transforming the different industries over the last few years. Availability of Big Data tools and analytics methodologies have changed the way in which data is being processed and analyzed. The modern form of data analytics is based on accurate data gathered using precise methods. As the data collection process is automated, it reduces the percentage of errors and chances of data manipulation. The adoption of digital engineering and technologies such as cloud computing, augmented reality, automation, and data analytics has catapulted the emergence of Industry 4.0. Industries are now looking for analytical solutions to effectively optimize their production and businesses process, increase production, and improve quality.

Why Data Analytics is Required for Industries?

Industries greatly benefit by using data analytics.

  • Data analytics converts raw data into actionable insights that will help improve productivity, quality and overall performance.
  • The analytics process helps identify problems and anomalies in the early stages, allowing you to immediately correct the issues.
  • Data analytics helps industries track and monitor the work queue, production time, and downtime. Based on this information, they can plan a production schedule that makes optimum use of the machines.
  • Analytics tools can be used to identify bottlenecks in the industrial process.
  • Data analytics can be used to set up a preventive maintenance program. The data collected from the systems allows technicians to identify any equipment malfunction in the early stages.
  • Analytics helps reduce process downtime, which will benefit your bottom line.
  • Information obtained from data analytics can be used to plan and schedule the production process.
  • Effectively analyze the quality of your products/services and ensure consistency with the help of analytics.
  • Data analytics maximizes the decision value. It is because the decisions are made based on accurate information gathered from reliable sources.
  • Data analytics can also be used to optimize your supply chain and logistics.
  • Inventory management is made more effective with the help of data analytics tools.
  • Analytics helps improve workforce effectiveness.

Data Analytics Services Provided by Utthunga

Predictive Analytics

Predictive analytics involves the use of mathematical methods and tools such as machine learning, data mining, statistical analysis, and predictive models. This analytics method is used to:

  • Identify anomalies in the process, which help in preventive maintenance.
  • Estimate the demand for product, raw material etc.: based on historical data and current scenario.
  • Forecast possible outcomes based on data obtained from the process.
Prescriptive Analytics

Prescriptive analytics is used to identify ways in which an industrial process can be improved. While predictive analytics tells when could a component/asset fail, prescriptive analytics tells what action you need to take to avoid the failure. So, you can use the results obtained from prescriptive analysis to plan the maintenance schedule, review your supplier, etc. Prescriptive analytics also helps you manage complex problems in the production process using relevant information.

Descriptive Analytics

The core purpose of descriptive analytics is to describe the problem by diagnosing the symptoms. This analytics method also helps discover the trends and patterns based on historical data. The results of a descriptive analytics are usually shown in the form of charts and graphs. These data visualization tools make it easy for all the stakeholders, even those who are non-technical to understand the problems in the manufacturing process.

Data Visualization

While numbers tell us accurately what is happening in the production line or in the supply chain, they don’t describe the context or give the overall picture. This is where data visualization comes in. Data visualization tools help convert complex data sets into understandable graphs and charts. The visual format can be presented to the stakeholders and decision makers who can now get a clear picture of the analyzed data and its implications. At Utthunga, we leverage Analytics, Prediction, Visualization technologies such as Tensor Flow, Azure Analytics, R / Python, Spark, Power BI, Node JS, Angular, XAML and others. Our customized data visualization solutions help companies get more from their analytics processes.

Diagnostic Analytics

Diagnostic analytics is also referred to as root cause analysis. While descriptive analytics can tell what happened based on historical data, diagnostic analytics tells you why it happened. Data mining, data discover, correlation, and down and drill through methods are used in diagnostic analytics. Diagnostic analytics can be used to identify cause for equipment malfunction or reason for the drop in the product quality.

predictive analytics

How Utthunga can help in Data Analytics?

For many years, the industry has been talking about the Industrial Internet of Things (IoT) and the ability to leverage the information coming off of these devices by digitizing the data of plant floor assets and manufacturing processes.

Utthunga has developed a very robust and scalable IIoT platform – Javelin which can connect field devices and other industrial assets to generate rich visualization and analytics.

We provide these services under analytics

  • Predictive Analytics
  • Prescriptive Analytics
  • Descriptive Analytics
  • Diagnostic Analytics
  • Data Visualization

FAQs

The major benefits of using predictive analysis for industries are:

  • Get accurate data for informed decision making.
  • Improve quality of the products.
  • Enable demand forecast.
  • Aid risk management process.
  • Setup preventive maintenance of production equipment.

Analytics enables stakeholders to use accurate information to optimize industrial processes. The actionable insights derived from analytics helps reduce operational and maintenance costs in the long run. Analytics also provides insights in terms of product and process quality as well as safety measures.

Industries can use prescriptive analytics to identify areas where the process can be improved. Prescriptive analytics can be used in maintenance planning, production scheduling/planning, inventory management, logistics, supply chain management and several other uses cases.

Descriptive analytics is used to discover new areas of growth and development. It also helps in diagnosing possible causes of errors or quality drop in the production process. Descriptive analytics is used in real-time data visualization based on variables and components of the Industrial process.

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